FEMA flood zone designation for an address or coordinate. Returns the zone code, plain-English risk, BFE if applicable, FIRM panel reference, and whether NFIP insurance is mandated for federally-backed mortgages.
| Name | Required | Description | Default |
|---|---|---|---|
| lat | No | ||
| lon | No | ||
| location | No | Address or zip to geocode. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover the read-only, non-destructive, idempotent nature of the call. The description adds useful behavioral detail by specifying exactly what the tool returns, including conditional information like 'BFE if applicable' and the NFIP insurance mandate. It does not mention edge-case behavior such as failed geocoding, but that is secondary given the safe read-only annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two tight sentences front-load the tool's core purpose and then enumerate the return value. There is no filler, redundant restatement of annotations, or unnecessary detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The output side is well covered for a tool with no output schema, including the key policy-relevant field about NFIP mortgage requirements. However, the input contract is incomplete: it does not define which parameter combination is required, does not explain lat/lon semantics, and omits likely caveats such as FEMA's U.S.-only coverage or geocoding failures. The definition is workable but not fully self-sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With only 33% schema description coverage and no required parameters, the description carries the burden of explaining lat/lon and location usage. 'Address or coordinate' hints at two input modes, but it never says whether to provide location alone, lat+lon together, or exactly one of either. An agent cannot confidently construct a correct call from this text alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names the resource ('FEMA flood zone designation'), the supported input types ('address or coordinate'), and the concrete return fields (zone code, risk, BFE, FIRM panel, NFIP mandate). This clearly distinguishes it from related lookups such as nfip_flood_claims or disaster declarations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'for an address or coordinate' implies the primary context, but the description does not explicitly state when to prefer this tool over related alternatives, nor does it mention exclusions such as geographic scope. Usage is inferable but not made explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Many tools overlap heavily across domains: caselaw_search vs court_case_search vs court_opinion_search, caselaw_citation_lookup vs court_citation_resolver, and a cluster of company due-diligence tools (company_trust_check, counterparty_risk_score, entity_dossier, issuer_diligence_dossier, kyb_aml_evidence_case_file) that all screen a company for sanctions/risk/standing. With 290 tools, an agent will frequently face multiple equally plausible choices for the same user intent.
The vast majority of tools follow a clean domain-prefix + snake_case pattern (census_, eia_, fmcsa_, npi_, cfpb_, etc.), but there are notable exceptions: entity_resolve and resolve_entity are reversed duplicates, reg_search (Federal Register) sits next to reg_cfr_search (CFR) with confusingly similar names, and carrier_monitor_recheck deviates from the carrier_vetting_* family.
290 tools is an extreme count under any rubric, far exceeding even the 50+ threshold for the lowest score. While the group-filtering mechanism and meta-tools like list_tool_groups and search_available_datasets mitigate the practical burden, the raw surface is still massively oversized for an agent to select from accurately and efficiently.
For a read-only data-aggregation server, coverage is remarkably comprehensive across 59 domains, and generic fallbacks like cdc_dataset_query, eia_series_lookup, fred_observations, and bls_series prevent most dead ends. Minor gaps exist (a single GitHub tool, demo-only property_lookup coverage, no write/update operations anywhere), but the stated data-access purpose is well served.